ABSTRACT Clostridium difficile infection (CDI) is one of the most prevalent and devastating healthcare-associated infections. Following standard antibiotic therapy, up to 25% of individuals with CDI develop one or more recurrences. Persistent or repeated episodes are difficult to treat and are a significant hardship for patients. The high success rate of fecal microbiota transplant for recurrent CDI provides powerful insight into the importance of restoring normal gut microbiota. However, to date, there are no microbiological or ?omic? (microbiomic, metagenomic, metabolomic) predictors of C. difficile recurrence. The objective of this proposal is to determine temporal dynamics of microbial profiles and ?omic? signatures associated with C. difficile recurrence. Our hypothesis is that patients who do not develop a C. difficile recurrence share an identifiable set of microbes, genes, and fecal metabolites in the gut microbiota. The rationale is that once the temporal dynamics of microbial and ?omic? signatures associated with C. difficile recurrence are well defined, candidate microbial or ?omic? biomarkers can be validated prospectively, ultimately allowing the development of strategies to prevent recurrent CDI. Specific preemptive therapy (e.g. microbiome manipulation) may then be developed on the basis of microbial compositions, genes, or metabolites of that microbiota. This novel approach offers an innovative method for preventing recurrent CDI. We will test the hypothesis by pursuing the following Specific Aims: 1) Determine the composition and structure of gut microbiota longitudinally in subjects following CDI, 2) Determine the metagenome of C. difficile gut microbiota, and 3) Perform global metabolomic analyses using LC-HRMS and GC-MS, including cholesterol and bile acid metabolites, to determine key metabolites in C. difficile gut microbiota. The approach is innovative because it will utilize a combination of unbiased, culture-independent 16S rRNA deep sequencing, metagenomic, and metabolomic approach and innovative computational techniques and multivariate statistical methods to identify ?omic? signatures in gut microbiota associated with C. difficile recurrence. The proposed research is significant because there are virtually no data on the relationship between gut microbiota, omic signatures and C. difficile recurrence. This proposal will define ?omic? signatures that can be validated in future studies, ultimately leading to novel strategies based on ?omic? profiles for primary prevention of recurrent CDI.